KR101828279B1 - Nano sensor for early diagonisis of lung cancer using surface enhanced raman scattering and the method thereof - Google Patents

Abstract

The present invention relates to an early diagnosis of lung cancer using surface enhanced Raman scattering and a method thereof, and a method of irradiating a sample with laser light to obtain a Raman scattering signal in early diagnosis of lung cancer using surface enhanced Raman scattering A laser irradiator for performing laser irradiation; A nanostructured chip in which metal nanoparticles amplifying a Raman scattering signal from a specimen are fixed on a silicon wafer; A spectroscope that receives a Raman scattering signal from the specimen and performs a function of obtaining and storing a spectrum therefrom; And an analysis unit for performing diagnosis of lung cancer through multivariate analysis of Raman scattering signals from the specimen on the nanostructured chip. The present invention relates to an early diagnosis nanomaterial for lung cancer using surface enhanced Raman scattering and a method thereof.

Description

TECHNICAL FIELD [0001] The present invention relates to an NANO sensor, and more particularly, to a NANO SENSOR FOR EARLY DIAGNOSIS OF LUNG CANCER USING SURFACE ENHANCED RAMAN SCATTERING AND THE METHOD THEREOF,

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a nanosensor using surface enhanced Raman scattering for early detection of lung cancer through surface enhanced Raman spectroscopy and a method thereof and more particularly to a nanosensor using metal nano- And a lung cancer diagnosis sensor that analyzes the Raman scattering signal from the irradiated laser light and analyzes it through a multivariate analysis method so that the lung cancer can be diagnosed early on the substrate (chip) And a method thereof.

Recently, as the incidence of cancer steadily increases and the importance of early detection has increased, various methods for diagnosing cancer have been proposed. In particular, lung cancer refers to malignant tumors originating from the lungs, with lung cancer deaths accounting for about 29% of all cancer deaths, accounting for the second highest proportion of all cancers, It is known to be high.

 In addition, the survival rate of patients with lung cancer over 5 years was not more than 17.4%, and the survival rate was more than 78% for patients who had early detection of lung cancer at the first stage. However, I have been diagnosed with lung cancer before.

Currently, diagnostic methods using X-ray, computed tomography (CT), and continuous wave are the diagnostic methods for cancer. Diagnostic techniques using X-ray , An X-ray light source and a photosensitive plate are disposed and a patient is positioned therebetween, and an image of organs inside the human body is acquired using the difference in transmission power of the X-ray, and the cancer is diagnosed by analyzing the image. Dimensional image of the tumor is represented as a two-dimensional film, and the sensitivity of the diagnosis of cancer is low.

On the other hand, the diagnostic method using CT also uses X-ray diffraction power difference like X-ray technique. Since the human body is scanned to acquire many tomographic images, the patient is 5 to 10 times Which is exposed to radiation.

In addition, the continuous wave diagnosis method is to diagnose lung cancer by measuring the amplitude and phase of the continuous wave, not the X-ray. In order to diagnose lung cancer, a frequency sweep is performed in a frequency band where analysis is required. It is disadvantageous in that the configuration of the transmission / reception circuit and the hardware associated therewith is complicated.

According to the above-mentioned circumstances, a new cancer diagnosis method which is faster, has higher sensitivity and is harmless to the human body is being researched and developed. Among them, the present invention relates to early detection of lung cancer through surface enhanced Raman spectroscopy techniques.

Korean Patent Laid-Open Publication No. 10-2012-0087051 (Aug. 6, 2012) discloses a biomarker and surface-enhanced Raman scattering dots (SERS dots) as a conventional technique of the present invention. More particularly, the present invention relates to a method for attaching a chemical / biological tag to a surface enhanced Raman scattering particle and biomarker of the disease in a target-oriented manner, Techniques for acquiring images thereof by optical imaging techniques to diagnose / discriminate diseases are described.

However, in the prior art, as in most of the SERS-related technologies, a method of binding a biomarker to Raman scattering particles is used to diagnose a disease by obtaining a SERS signal, which complicates the diagnosis process.

Korean Patent Laid-Open No. 10-2015-0036345 (Apr. 05, 2015) relates to a method relating to real-time cancer diagnosis by an endoscopic examination using an optical fiber Raman spectroscopy, and more particularly, to a method of diagnosing a cancer by using a laser light source, a spectroscope, And transmitting the light from the laser light source to a target and restoring the scattered light to the spectrometer, the method comprising: transmitting light from a laser light source to a standard target having a known spectrum; recording a black spectrum of the scattered light from a standard target; Comparing the known spectrum with a calibration system, generating a transfer function, and then storing the transfer function.

The prior art is a method of diagnosing cancer by analyzing Raman scattering signals obtained directly from the lungs using an endoscope and diagnosing cancer by obtaining a general Raman signal instead of the SERS signal due to the limit of the endoscope, .

The present invention has been made in order to solve the above problems, and it is an object of the present invention to provide an apparatus and a method for manufacturing a semiconductor device in which a bronchial washing liquid is dropped on a substrate having gold, silver or copper metal nanoparticles fixed on a silicon wafer, And to provide a method for early detection of lung cancer using the surface enhanced Raman scattering, which performs lung cancer diagnosis using multivariate analysis, and a method thereof.

An early detection lung cancer nanosensor using surface enhanced Raman scattering according to an embodiment of the present invention includes a laser irradiation device that performs a role of irradiating a specimen with laser light to obtain a Raman scattering signal; A nanostructured chip in which metal nanoparticles amplifying a Raman scattering signal from a specimen are fixed on a silicon wafer; A spectroscope that receives a Raman scattering signal from the specimen and performs a function of obtaining and storing a spectrum therefrom; And an analyzer for performing lung cancer diagnosis by comparing and analyzing spectra obtained from the spectroscope with respect to a cutoff value set based on the Raman scattering signal spectrum of the statistically treated normal human group and lung cancer patient group, And the bronchial washing solution is a bronchial washing solution obtained by washing the bronchial tissue using the same.

In addition, the cut-off value set by the analysis unit is used to calculate peak-to-peak (PCA: Principal Component) values for peaks other than the reference peak, which is a peak representing DNA / RNA in the peaks of the spectrum obtained from a sample of a large number of normal persons and lung cancer patients. Analysis to obtain a eigenvalue of a principal component vector; Selecting principal component vectors that sequentially select eigenvalues of the principal component vectors in descending order and accumulate eigenvalue values of principal component vectors equal to or greater than a predetermined value; Performing Linear Discriminant Analysis (LDA) further including at least one or more variables selected from the group consisting of gender, age and smoking status together with the selected main ingredient; Obtaining a posterior probability of the sample obtained in the linear discriminant analysis; Determining a cutoff value that maximizes sensitivity and specificity from the posterior probability distribution of the normal person group and the posterior probability distribution of the lung cancer patient group.

In addition, the analysis unit may perform Principal Component Analysis (PCA) on the spectrum of the specimen obtained from the spectroscope, except for the reference peak which is the peak indicating the DNA / RNA in the peak of the spectrum, Obtaining an eigenvalue; Selecting principal component vectors that sequentially select eigenvalues of the principal component vectors in descending order and accumulate eigenvalue values of principal component vectors equal to or greater than a predetermined value; Performing Linear Discriminant Analysis (LDA) further including at least one or more variables selected from the group consisting of gender, age and smoking status together with the selected main ingredient; If the posterior probability obtained from the linear discriminant analysis is higher than the cutoff value, it is judged to be lung cancer, and if it is lower than the cutoff value, it is judged that it is normal.

Meanwhile, the metal nanoparticles may be any one selected from the group consisting of gold nanoparticles, silver nanoparticles, and copper nanoparticles, and may have any one or more of a rod shape, a crumpled shape, and a central satellite shape .

According to another embodiment of the present invention, there is provided a method for early diagnosis of lung cancer using surface enhanced Raman scattering, comprising: placing a specimen on a substrate on which a metal nanoparticle amplifying a Raman scattering signal is fixed on a silicon wafer; Irradiating the specimen placed on the substrate with a laser beam using a laser irradiation apparatus; Receiving a Raman scattering signal from the specimen by the laser beam using a spectroscope to obtain a spectrum drum; And performing lung cancer diagnosis by comparing and analyzing spectra obtained from the spectroscope with respect to a cutoff value set based on the Raman scattering signal spectrum of the statistically treated normal human group and lung cancer patient group.

In addition, in the step of performing the lung cancer diagnosis, the set cut-off value is determined by performing principal component analysis on peaks except for a reference peak which is a peak indicating DNA / RNA in a peak of the spectrum obtained from a sample of a large number of normal persons and lung cancer patients (PCA: Principal Component Analysis) to obtain a principal component vector eigenvalue; Selecting principal component vectors that sequentially select eigenvalues of the principal component vectors in descending order and accumulate eigenvalue values of principal component vectors equal to or greater than a predetermined value; Performing Linear Discriminant Analysis (LDA) further including at least one or more variables selected from the group consisting of gender, age and smoking status together with the selected main ingredient; Obtaining a posterior probability of the sample obtained in the linear discriminant analysis; Determining a cutoff value that maximizes sensitivity and specificity from the posterior probability distribution of the normal person group and the posterior probability distribution of the lung cancer patient group.

The step of performing the diagnosis of lung cancer may further comprise performing Principal Component Analysis (PCA) on the spectra of the specimen obtained from the spectroscope, with respect to the peaks excluding the reference peak which is the peak indicating the DNA / RNA in the peak of the spectrum To obtain an eigenvalue of a principal component vector; Selecting principal component vectors that sequentially select eigenvalues of the principal component vectors in descending order and accumulate eigenvalue values of principal component vectors equal to or greater than a predetermined value; Performing Linear Discriminant Analysis (LDA) further including at least one or more variables selected from the group consisting of gender, age and smoking status together with the selected main ingredient; Judging lung cancer if the posterior probability obtained from the linear discriminant analysis is higher than the cutoff value and judging normal if the posterior probability is lower than the cutoff value.

The present invention relates to an early diagnosis of lung cancer using surface enhanced Raman scattering and a method thereof, and more particularly, to a method and apparatus for detecting a lung cancer by irradiating a bronchial washing solution as a specimen on a substrate (chip) on which metal nano- By analyzing the Raman scattering signal from the irradiated laser light by using multivariate analysis method, it is possible to diagnose lung cancer early, and thus, it is possible to perform the preliminary treatment without complicated process, high cost biomarker, antigen, It is possible to diagnose lung cancer with high sensitivity.

In addition, since the time spent in the analysis is less than one minute in the present invention, it is possible to greatly reduce the time consumed in diagnosis of lung cancer as compared with the prior art.

FIG. 1 is a view for explaining a configuration of an early detection lung cancer nanosensor using surface enhanced Raman scattering according to an embodiment of the present invention.
FIG. 2 is a view for explaining the structure of a nanostructured chip of an early detection lung cancer nanosensor using surface enhanced Raman scattering according to an embodiment of the present invention. FIG.
3 is an exemplary view for explaining the shape of metal nanoparticles on a nanostructured chip according to an embodiment of the present invention.
4 is a graph showing an electron microscope image and optical characteristics of gold nanoparticles according to an embodiment of the present invention.
5 is a graph showing a spectrum of a Raman scattering signal measured from a bronchial washing solution of a normal person and lung cancer patients using a lung cancer early detection sensor according to an embodiment of the present invention.
FIG. 6 is a graph summarizing the differences of the graphs of the cancer patients with respect to the normal graph among the characteristic peaks shown in FIG.
FIG. 7 is a graph of Recover Operation Characteristic (ROC) illustrating performance of the lung cancer early detection sensor according to an embodiment of the present invention.
8 is a diagram for explaining distribution and cutoff value of a posterior probability of a normal group and a lung cancer patient group according to an embodiment of the present invention.
9 is a flowchart illustrating an early diagnosis method of lung cancer using surface enhanced Raman scattering according to an embodiment of the present invention.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. In order to easily implement the present invention, the surface enhanced Raman scattering- The embodiment will be described in detail.

In the drawings of the present invention, the sizes and dimensions of the structures are enlarged or reduced from the actual size in order to clarify the present invention, and the known structures are omitted so as to reveal the characteristic features, and the present invention is not limited to the drawings .

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the following description, well-known functions or constructions are not described in detail to avoid obscuring the subject matter of the present invention.

In addition, since the embodiments described in the present specification and the configurations shown in the drawings are only the most preferred embodiments of the present invention and do not represent all the technical ideas of the present invention, It is to be understood that equivalents and modifications are possible.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, a lung cancer early diagnosis nanosensor and a method thereof using surface enhanced Raman scattering according to the present invention will be described in detail with reference to the accompanying drawings.

FIG. 1 is a view for explaining a configuration of an early detection lung cancer nanosensor using surface enhanced Raman scattering according to an embodiment of the present invention.

An early detection lung cancer nanosensor using surface enhanced Raman scattering according to the present invention comprises a laser irradiation device, a nanostructured chip, a spectroscope and an analysis part, wherein the laser irradiation device comprises a nanostructure And irradiates the specimen on the chip with laser light.

In addition, the nanostructured chip has a sample positioned therein and amplifies a Raman scattering signal emitted from the specimen by the laser beam, and outputs the amplified Raman scattering signal to a surface enhanced Raman scattering (SERS) signal.

The spectroscope receives the amplified Raman scattering signal from the specimen on the nano-structured chip by the laser light, and obtains and stores a spectrum therefrom. The analyzing unit performs multivariate analysis on the spectra obtained in the spectroscope, Of the disease.

In the present invention, Principal Component Analysis (PCA) is used as the multivariate analysis. For reference, the above principal component analysis technique can be applied to a case where there are many observations (multivariate) for one observation object, and when it is impossible to grasp even one by one or two correlations, It is a statistical technique to extract the principal component expressing the pattern concisely as the linear combination of the original variables.

In the present invention, data is processed with the peak of the spectrum obtained from the Raman scattering signal as a variable.

It is possible to distinguish normal people from lung cancer patients by performing Linear Discriminant Analysis (LDA) on the data thus processed. For reference, the linear discriminant analysis is an analysis method in which a function is formed to discriminate which population belongs to each case when samples from two or more populations are mixed.

For reference, in the sensor for early detection of lung cancer using the surface enhanced Raman scattering of the present invention, a bronchial washing solution in which a bronchial washing is performed using physiological saline or the like is used as a specimen.

On the other hand, the nanostructured chip is formed with metal nanoparticles for amplifying a Raman signal emitted from a specimen.

FIG. 2 is a view for explaining the structure of a nanostructured chip of an early detection lung cancer nanosensor using surface enhanced Raman scattering according to an embodiment of the present invention. FIG.

As shown in FIG. 2, the nanostructured chip of the present invention is a form in which metal nanoparticles are fixed on a silicon wafer. The nanostructure formed of the metal nanoparticles has a maximum Raman scattering signal of up to one billion Amplified. The Raman scattering signal can be used to determine the configuration information of the biomaterial. In the present invention, the presence or absence of the disease can be determined by detecting a change in the biomolecule with high sensitivity based on the large signal amplification rate on the nano-structured chip.

The nanostructured chip is obtained by fixing a metal nanoparticle on a silicon wafer using a room-temperature drying method or an inorganic or organic fixing material. The metal nanoparticle may be a gold (Au) nanoparticle, a silver (Ag) ) Nanoparticles may be selected and used.

FIG. 3 is a view for explaining the shape of metal nanoparticles on a nanostructured chip according to an embodiment of the present invention. As shown in FIG. 3, the metal nanoparticles of the present invention are used to improve the amplification rate of Raman scattering signals Structure, which can be selected from at least one of a rod shape, a crumpled shape, and a central satellite shape in which a plurality of nanoparticles are clustered toward one central portion.

Manufacturing example

1. Fabrication of nanostructured chip with rod-shaped gold nanoparticles

First, the rod-shaped gold nanoparticles are prepared by growing in the early spherical gold nanoparticles.

The initial spherical gold nanoparticles are prepared by adding 25 μL of 0.1 M HAuCl 4 and 0.6 mL of 0.01 M NaBH 4 solution to 10 mL of 0.1 M CTAB solution and stirring for 2 minutes.

The gold nanorods were grown by adding the growth solution to the spherical gold nanoparticles prepared as described above. The growth solution was prepared by adding 1 mL of 0.004 M AuNO 3 and 0.7 mL of 0.079 M ascorbic acid to 99.5 mL of a 0.1 M CTAB solution .

When 25 μL of the initial spherical gold nanoparticle solution is added to the above-mentioned growth solution without impact for one day, the spherical gold nanoparticles in the solution grow into a bar-like shape.

The gold nanorods thus prepared are placed on a silicon wafer substrate that does not affect the Raman signal and are dried at room temperature or fixed using an organic or inorganic material that does not cause signal distortion, thereby amplifying the Raman scattering signal from the sample The nanostructured chip of the present invention is produced.

2. Fabrication of nanostructured chip with rod-shaped gold nanoparticles

Crumpled nanoparticles are prepared by immobilizing and growing early spherical gold nanoparticles. The initial spherical nanoparticles may be commercially available 4-5 nm gold nanoparticles or may be spherical gold nanoparticles prepared by the same method as the initial spherical gold nanoparticles used in manufacturing the above-mentioned rod-shaped gold nanoparticles.

The initial spherical gold nanoparticles prepared are immobilized using other organic or inorganic fixing materials depending on the material to be fixed. In the case of the silicon wafers used in the examples, the silicon wafers were treated with 5 mM MPTMS for 12 hours as an organic substance having an amine group or a mercapto group And fixed.

The growth solution is prepared by adding 200 μL of 0.05 M HAuCl 4 solution and 1.2 mL of 7 mM AgNO 3 solution to 40 mL of 0.05 M CTAB solution, and finally adding 300 μL of 0.1 M ascorbic acid.

When the prepared initial spherical nanoparticles are immobilized on the silicon wafer for 5 hours at 30 ° C, they grow in a crumpled form.

3. Center-top molding gold - Example of nanostructured chip with platinum nanoparticles formed

In the case of platinum nanoparticles, the center gold nanoparticles can be produced by reducing platinum to the central gold nanoparticles. In the case of the central gold nanoparticles, various sizes can be manufactured.

Add 20 mL of 0.2 M CTAB solution to 20 mL of center gold nanoparticle solution, add 0.2 M ascorbic acid after stirring, and heat well until boiling. After the addition of H2PtCl6, heat is applied for about 1 hour so that the reduction takes place sufficiently. The amount of platinum satellites is determined by the weight ratio of H2PtCl6 to gold nano added.

4 is a graph showing electron microscope images and optical characteristics of gold nanoparticles according to an embodiment of the present invention. FIG. 4 (a) is a graph showing the optical characteristics of the gold nanoparticles produced, showing that the rod-shaped gold nanoparticles have a more appropriate light absorption area than the spherical gold nanoparticles when using a 785 nm laser.

In addition, as shown in Fig. 4 (b), when the rod-shaped gold nanoparticles were formed on silicon rather than silicon, the Raman signal intensity was measured to be higher, and the measurement was also performed from the gold nanoparticle- The intensity of the Raman signal was measured to be higher than the intensity of the Raman signal measured from the silicon phase sample. From this, it can be seen that the Raman signal is amplified by the metal nanoparticles.

That is, by forming metal nanoparticles on a nanostructured chip, the present invention can amplify a Raman scattering signal emitted from a sample, sensitively detect the change, and measure changes in a biological material, thereby enabling high-accuracy pathological prediction do.

FIG. 5 is a graph showing a spectrum of a Raman scattering signal measured from a bronchial washing solution of a normal person and lung cancer patients using an early detection sensor for lung cancer according to an embodiment of the present invention. FIG. 6 is a graph showing a spectrum of characteristic peaks This is a graph summarizing the difference of the graph of the cancer patient against the normal graph. These characteristic peaks are individually meaningful, indicating lipid at 1389 cm ^-1 and DNA / RNA at 1581 cm ^-1 , respectively.

As shown in FIG. 5 and FIG. 6, the lung cancer early detection sensor of the present invention can discriminate patients such as normal people from lung cancer by comparing a plurality of peaks according to the wavelength of the Raman scattering signal.

As described above, statistical treatment is required because each peak has some degree of individual significance but the entire patient does not exhibit the same pattern. That is, if each peak is set as a variable, then it is a dispersion pattern of these variables that are compared in the statistical processing.

More specifically, the lung cancer early diagnosis sensor of the present invention collects data on the characteristic peak difference in the collected Raman signal, as shown in FIG.

The characteristic peaks were 16 in total from 455 cm ^-1 due to the circular vibration of the material to 1581 cm ^-1 by DNA / RNA, but the peaks of 1581 cm ^-1 among them showed no difference between normal and cancer patients It is used as a comparison of the concentration of bronchial washing fluid. For lung cancer diagnosis, 15 peaks up to 1450 cm ^-1 by protein are used.

Quantify the 16 peak is peak in Figure 455 of the peak present in the 5, 486, 553, 763, 817, 884, 890, 1005, 1029, 1125, 1151, 1192, 1249, 1389, 1450, 1581 cm -1 FIG. 6 is a graph showing the difference between the mean value of the normal person and the cancer patient and the value of the cancer patient when the normal person is set to zero. In the case of the color value not shown in FIG. 6, there is no indication because there is no difference between the normal person and the cancer patient or is small.

1581 peak is the peak of DNA / RNA, and it is assumed that the cancer patient will have the same value as the normal person. For example, in the case of cancer, the lipid-induced peak, 1389, may be altered because of cell deformation, but the DNA level is not altered by cancer.

The reason for taking the reference peak is that the used sample is a bronchial washing solution. In this case, since the patient may have different concentration values, standard peaks are required for standardization.

If the reference peak is high, the concentration of the substance in the bronchial washing solution is high rather than being deformed by the cancer. Therefore, if the data is divided according to the reference peak before data processing, the concentration in the bronchial washing solution may be all the same.

That is, data for a total of 15 peaks excluding the reference peak at a total of 16 peaks, and it is analyzed by using principal component analysis (statistical program, simple program using matlab and C language formed by linear analysis code, etc.) (PCA), 15 principal component vectors are obtained as shown in Table 1 below.

Eigenvalue Cumulative One 9.351 0.6234 2 2.797 0.8099 3 1.100 0.8832 4 0.566 0.9210 5 0.447 0.9508 6 0.197 0.9639 7 0.160 0.9745 8 0.137 0.9837 9 0.104 0.9906 10 0.053 0.9941 11 0.036 0.9965 12 0.024 0.9981 13 0.014 0.9990 14 0.008 0.9996 15 0.007 1.0000

When the main component is selected in order of higher eigenvalues so that the cumulative value is more than 0.95, 5 main components (5 PCs) are selected, and three variables such as sex, smoking status and age are added to the total 8 variables A linear discriminant analysis (LDA) was performed to determine whether cancer and normal subjects were well distinguished. The results were 85.71% accuracy, 84.21% sensitivity, and 87.5% specificity.

In this case, only 5 of them can represent 95%. If the distribution of data is disturbed, more main ingredient may be needed and more than 5 main ingredient may be used for more accurate analysis. For example, in Table 1 above, if you want to get more than 99% accuracy, you should use 9 main components.

)와 분산-공분산 행렬(

)을 계산하여 각 관측치와 각 집단까지의 일반화자승거리(generalized squared distance) 함수를 이용하여 집단 내 변동 (분산)에 비해 집단 간 변동의 차이를 최대화하는 예측변수들의 선형판별식을 만들어 개체의 집단을 판별하는데 이용하는 통계 기법 중 하나이다. For reference, the LDA assumes a multivariate normal distribution and uses the mean vector () for the same variables measured in each pre-

) And a variance-covariance matrix (

), And using a generalized squared distance function to each observation and each group, a linear discriminant of the predictive variables that maximize the difference of the inter-group variation compared to the intra-group variation (dispersion) Is one of the statistical techniques used to determine

The following is the classification of each individual into groups.

)로서, 일반화자승거리계산 시 반영되어 사후확률 (

,

: posterior probability)이 계산되고, 개인별로 각 그룹에 속할 사후확률이 제시되며 사후확률 값이 큰 그룹으로 분류된다. The ratios of each group of data used in the model construction are given by the prior probability,

), Which is reflected in the calculation of the generalized squared distance,

,

: Posterior probability is calculated, posterior probability belonging to each group is presented to individual, and posterior probability value is classified into large group.

FIG. 7 is a ROC (Receiver Operation Characteristic) graph obtained by collecting data from a total of 33 patients including 16 lung cancer patients and 17 non-lung cancer patients. The lower the area (AUC) of the ROC graph is closer to 1, the more predictive the lung cancer diagnosis is.

Here, the ROC graph can be drawn using the sensitivity and specificity obtained from all possible posterior probabilities, and the AUC value is obtained using the program. Based on the index of Youden, the sensitivity and specificity The cutoff value was set to 0.646.

The lung cancer early detection sensor according to the present invention predicts cancer patients when the postoff probability of each patient is higher than the cutoff value (0.646). As a result, For patients who were diagnosed with cancer, a rapid positive diagnosis was possible.

No Actual patient status Primary clinical outcome Second Clinical Outcome Posterior probability (SERS) One Adenocarcinoma voice suspicion 0.99926 2 Sq. cell ca. voice Confirmed 0.89511 3 Sq. cell ca. voice voice 0.00008 4 Sq. cell ca. voice voice 0.10792 5 Sq. cell ca. voice voice 0.40721 6 Small cell ca. voice Confirmed 0.99880

That is, when a test is performed on a bronchial washing liquid sample of a cancer patient or a normal person, 16 liquid Raman peaks can be obtained by analyzing a liquid sample through the lung cancer early diagnosis nanosensor, The posterior probability is calculated by entering the LDA model with sex, age and smoking information.

For example, assuming that the distribution of posterior probabilities of normal and lung cancer patients is as shown in FIG. 8, the sensitivity and specificity are different depending on how the cutoff value of the posterior probability of x axis is defined. In Fig. 8, TP indicates True Positive, TN indicates True negative, FP indicates False Positive, and FN indicates False Negative.

In the present invention, the cutoff value is determined by finding a point at which the sum of the sensitivity and the specificity becomes maximum (Youden's index), the posterior probability at this time is calculated as 0.646, and the calculated cutoff value Lung cancer was used as a diagnostic criterion.

That is, if the posterior probability value is 0.646 or more, it is diagnosed as lung cancer, and if it is smaller than 0.646, it is judged as normal.

In addition, the lung cancer early detection sensor of the present invention can differentiate the types of lung cancer such as small cell cancer, squamous cell cancer, and adenocarcinoma through the above-described method, and can diagnose diseases other than cancer such as pneumonia.

The present invention may be applied to diagnosis of various diseases other than lung cancer by using the above-mentioned principle.

9 is a flowchart illustrating an early diagnosis method of lung cancer using surface enhanced Raman scattering according to an embodiment of the present invention.

9, an early diagnosis method for lung cancer using surface enhanced Raman scattering according to the present invention is a method for diagnosing lung cancer early using a surface enhanced Raman scattering according to the present invention. First, on a nanostructured chip on which a metal nanoparticle for amplifying a Raman scattering signal is immobilized on a silicon wafer, The washing liquid is placed (S101).

The sample placed on the nanostructured chip is irradiated with laser light using a laser irradiation apparatus (S102).

The Raman scattering signal from the specimen is received by the laser beam using a spectroscope to obtain a spectrum drum (S103).

Multivariate analysis is performed on the spectrum of Raman scattering light from the specimen obtained through the spectroscope to perform pathological prediction such as diagnosis of lung cancer (S104).

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, I will understand the point. Accordingly, the technical scope of the present invention should be determined by the following claims.

100: Early diagnosis of lung cancer nanosensor
110: laser light irradiation device
120: Nanostructured chip
130: spectroscope
140:

Claims (7)

In early diagnosis of lung cancer using surface enhanced Raman scattering,
A laser irradiator for performing a function of irradiating a specimen with a laser beam to obtain a Raman scattering signal;
A nanostructured chip in which metal nanoparticles amplifying a Raman scattering signal from a specimen are fixed on a silicon wafer;
A spectroscope that receives a Raman scattering signal from the specimen and performs a function of obtaining and storing a spectrum therefrom; And
And an analyzer for performing lung cancer diagnosis by comparing and analyzing spectra obtained from the spectroscope with respect to a cutoff value set based on the Raman scattering signal spectrum of the statistically treated normal person and lung cancer patient group, Wherein the bronchial washing solution is a bronchial washing solution which has been used to remove bronchial irritation.
The method according to claim 1,
The cut-off value set in the analysis unit is determined by Principal Component Analysis (PCA) on peaks except for a reference peak which is a peak representing DNA / RNA in a peak of the spectrum obtained from a sample of a large number of normal persons and lung cancer patients. To obtain an eigenvalue of a principal component vector;
Selecting principal component vectors that sequentially select eigenvalues of the principal component vectors in descending order and accumulate eigenvalue values of principal component vectors equal to or greater than a predetermined value;
Performing Linear Discriminant Analysis (LDA) further including at least one or more variables selected from the group consisting of gender, age and smoking status together with the selected main ingredient;
Obtaining a posterior probability of the sample obtained in the linear discriminant analysis;
Early detection of lung cancer using surface enhanced Raman scattering determined by including posterior probability distribution of normal subjects and posterior probability distribution of lung cancer patients to determine the cutoff value that maximizes sensitivity and specificity. The method according to claim 1,
The diagnosis of lung cancer in the analysis unit is performed by performing Principal Component Analysis (PCA) on the spectrum of the specimen obtained from the spectroscope except for the reference peak which is the peak indicating the DNA / RNA in the spectrum peak, To obtain an eigenvalue of the eigenvalue;
Selecting principal component vectors that sequentially select eigenvalues of the principal component vectors in descending order and accumulate eigenvalue values of principal component vectors equal to or greater than a predetermined value;
Performing Linear Discriminant Analysis (LDA) further including at least one or more variables selected from the group consisting of gender, age and smoking status together with the selected main ingredient;
Wherein the lung cancer is judged to be lung cancer if the posterior probability obtained from the linear discriminant analysis is higher than the cutoff value and if the posterior probability is judged to be normal when the cutoff value is lower than the cutoff value, early diagnosis of lung cancer using the surface enhanced Raman scattering. The method according to claim 1,
The metal nano-
Gold nanoparticles, silver nanoparticles, and copper nanoparticles.
Wherein the metal nanoparticles have at least one of a rod shape, a crumpled shape, and a central satellite shape.
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